STIHL Benelux: when basic data sets reveal rich customer insights

When it comes to identifying new customer bases, pinpointing their needs and developing strategies to attract them , understanding the behavior of existing customers is invaluable. But when companies – like STIHL Benelux – rely on networks of independent partners to sell their products, end customer data is hard to come by. “But that doesn’t mean we can’t unlock highly valuable data insights – with a little creative thinking,” asserts delaware.ai data scientist Kevin De Beck.

STIHL is a global manufacturer of high-quality gardening and landscaping tools. Its regional subsidiary, STIHL Benelux, works closely with dealers spread across Belgium, the Netherlands and Luxembourg to sell to consumers.

No customer data? No problem

As a B2B company that works with independent partner dealers that tend to be protective of customer data, STIHL Benelux knows little about the end users of their tools. But despite lacking the customer behavior insights that B2C companies can typically rely on, STIHL Benelux still had interesting sources of data to explore.

“They wanted to learn more about their Belgian customers by combining dealer transactional data with the results of a dealer-distributed market survey,” Kevin continues. “The survey was very useful, as it included questions about dealership size, number of parking spots available and the proximity of competitors, among others. We could easily pinpoint the locations of competing dealers through web searches.”

At that point, the creative spirit of the delaware.ai team took a place at the table. Kevin: “We figured that the volume of gardening waste collected in an area was a pretty good indicator of demand for gardening tools, and found an open data source containing this information by location.”

Painting a clear picture of demand using multiple resources

Once all the data had been identified and processed, the delaware.ai team applied advanced statistical approaches to identify the regions in Belgium with high or low sales potential – all based on dealer demand, the presence (or absence) of competitors, and the unique characteristics of each dealership. Then, they took their insights a step further.

“We took a close look at each dealer’s sales figures for individual STIHL products to find out which of them were over- or underperforming. This offers STIHL Benelux unique insights into where to potentially establish new dealer partnerships, investigate why some products are popular, where and why, and target their digital marketing approach accordingly,” Kevin goes on to say. “Despite a limited understanding of end-customer behavior, we helped STIHL uncover insights that it can act on today.”

Moving to the next level

In the process of performing the analyses, some surprising insights were highlighted. “For example, some competition around enhances the sales figures of a dealer. STIHL-only stores were surprisingly not as effective as dealers offering multiple brands, which could indicate that choice is important to consumers. We also found that very large stores tend to show lower sales of STIHL products.”

The next step? “STIHL Benelux could use the data insights we uncovered to potentially predict sales for the next season, or create a kind of template for the ideal dealership. The company could even share marketing insights and techniques with its independent partners to help them boost sales figures to benefit all parties,” Kevin concludes.

This case highlights the very beginning of the ‘get-keep-increase’ sales process optimization storyline . Discover five more detailed, high-impact, real-life delaware.ai case stories by downloading the full e-book.